Task decomposition using pattern distributor [PDF]
, 2004 In this paper, we propose a new task decomposition method for multilayered feedforward neural networks, namely Task Decomposition with Pattern Distributor in order to shorten the training time and improve the generalization accuracy of a network under ...
Guan, SU, Neo, T, Bao, C
core +7 more sources
Unbalanced Decision Trees for Multi-class Classification [PDF]
, 2007 In this paper we propose a new learning architecture that we call Unbalanced Decision Tree (UDT), attempting to improve existing methods based on Directed Acyclic Graph (DAG) and One-versus-All (OVA) approaches to multi-class pattern classification tasks.
Suppharangsan, Somjet +2 more
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Development of novel automated language classification model using pyramid pattern technique with speech signals [PDF]
, 2022 Language classification using speeches is a complex issue in machine learning and pattern recognition. Various text and image-based language classification methods have been presented. But there are limited speech-based language classification methods in
U. Rajendra Acharya +9 more
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Reduced pattern training based on task decomposition using pattern distributor [PDF]
, 2007 Task Decomposition with Pattern Distributor (PD) is a new task decomposition method for multilayered feedforward neural networks. Pattern distributor network is proposed that implements this new task decomposition method.
Guan, SU, Neo, T, Bao, C
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Formalization of the classification pattern: Survey of classification modeling in information systems engineering [PDF]
, 2016 Formalization is becoming more common in all stages of the development of information systems, as a better understanding of its benefits emerges. Classification systems are ubiquitous, no more so than in domain modeling.
Mitchell, A +7 more
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Classification of RCS sequences based on KL divergence
The Journal of Engineering, 2019 Radar cross section (RCS) is an important characteristic of radar targets. The mean, variance, skewness, kurtosis, varying patterns of RCS sequences provide rich features for radar target classification.
Qiang Cheng, Li Chen, Yaolin Zhang
doaj +1 more source
Sparse Deep Tensor Extreme Learning Machine for Pattern Classification
IEEE Access, 2019 A novel deep architecture, the sparse deep tensor extreme learning machine (SDT-ELM), is presented as a tool for pattern classification. In extending the original ELM, the proposed SDT-ELM gains the theoretical advantage of effectively reducing the ...
Jin Zhao, Licheng Jiao
doaj +1 more source
Classification-Friendly Sparse Encoder and Classifier Learning
IEEE Access, 2020 Sparse representation (SR) and dictionary learning (DL) have been extensively used for feature encoding, aiming to extract the latent classification-friendly feature of observed data.
Chunyu Yang +3 more
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Ensemble learning‐based classification of microarray cancer data on tree‐based features
Cognitive Computation and Systems, 2021 Cancer is a group of related diseases with high mortality rate characterized by abnormal cell growth which attacks the body tissues. Microarray cancer data is a prominent research topic across many disciplines focused to address problems related to the ...
Guesh Dagnew, B.H. Shekar
doaj +1 more source
Hierarchical incremental class learning with reduced pattern training [PDF]
, 2006 Hierarchical Incremental Class Learning (HICL) is a new task decomposition method that addresses the pattern classification problem. HICL is proven to be a good classifier but closer examination reveals areas for potential improvement.
Guan, SU +5 more
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